Installing relevant packages
library(tidyverse)
library(DT)
library(plotly)
library(lubridate)
library(dygraphs)
library(xts) # To make the convertion data-frame / xts format
Importing data
df_data <- read.csv('activity.csv',header=TRUE)
Preprocessing
Task 2: Average daily activity pattern
## [1] "Interval with the highest daily mean steps"
## # A tibble: 1 × 2
## interval mean
## <int> <dbl>
## 1 835 206.
Task 3: Imputing missing data
## [1] "Total missing days:"
## [1] 2304
## steps date interval
## 1 1.7169811 2012-10-01 0
## 2 0.3396226 2012-10-01 5
## 3 0.1320755 2012-10-01 10
## 4 0.1509434 2012-10-01 15
## 5 0.0754717 2012-10-01 20
## 6 2.0943396 2012-10-01 25